Prospective multicenter study of heart rate variability with ANI monitor as predictor of mortality in critically ill patients with COVID-19

The purpose of this study is to demonstrate that the most critically ill patients with COVID-19 have greater autonomic nervous system dysregulation and assessing the heart rate variability, allows us to predict severity and 30-day mortality. This was a multicentre, prospective, cohort study. Patients were divided into two groups depending on the 30-day mortality. The heart rate variability and more specifically the relative parasympathetic activity (ANIm), and the SDNN (Energy), were measured. To predict severity and mortality multivariate analyses of ANIm, Energy, SOFA score, and RASS scales were conducted. 112 patients were collected, the survival group (n = 55) and the deceased group (n = 57). The ANIm value was higher (p = 0.013) and the Energy was lower in the deceased group (p = 0.001); Higher Energy was correlated with higher survival days (p = 0.009), and a limit value of 0.31 s predicted mortalities with a sensitivity of 71.9% and a specificity of 74.5%. Autonomic nervous system and heart rate variability monitoring in critically ill patients with COVID-19 allows for predicting survival days and 30-day mortality through the Energy value. Those patients with greater severity and mortality showed higher sympathetic depletion with a predominance of relative parasympathetic activity.

influenced by the sympathetic nervous system and baroreflex mechanisms, although the influence of parasympathetic activity is also partially present; and, lastly, the very-low-frequency (VLF) component, between 0.003 and 0.04 Hz, is influenced by thermoregulation and different hormonal factors 13 . The HF variations of heart rate can also be expressed in a normalized unit (HFnu) and are usually defined as the ratio between the absolute value of the HF and the sum of HF and LF, i.e. HFnu = HF/(HF + LF). It, therefore, expresses the amount of parasympathetic variation over to the whole variability excluding VLF 6,[14][15][16] .
The parameters of HRV were recorded using the ANI monitor (MDoloris Medical Systems, Loos, France). The ANI monitor displays the Energy value which is equivalent to the SDNN index. Indeed, it exists a linear mathematical relation between Energy and SDNN as shown in the following formulas: ANI is an index related to the relative parasympathetic activity which analyses the magnitude of the normalized HF oscillations. It is computed by a graphical method calculating the area under the curve (AUC) of the oscillations produced by the RSA, i.e., HF oscillations, as it has been described by Logier et al. 15 and Jeanne et al. 16 .
For Energy and ANI computation, the RR intervals are isolated in a 64 s moving window. The mean RR value and the Energy are computed in this window. RR series is then centered by subtracting the mean value from each RR interval and normalized by dividing each RR interval by the Energy. The mean-centered RR series is then filtered between 0.15 and 0.4 Hz in order to obtain a signal representative of the HF oscillations. The envelopes between local maximum and minimum are then plotted (Fig. 1) and the area between the upper and lower envelopes is computed.
The area is then divided into 4 sub-areas A1, A2, A3, and A4 and AUCmin is defined as the lower sub-area. AUCmin = min (A1, A2, A3, A4). ANI is then defined as ANI = 100 × (a × AUCmin + b)/12.8 where a = 5.1 and b = 1.2 were determined on a 200 patients dataset to get a value between 0 and 100. ANI is then averaged over 56 s in order to obtain the instantaneous ANI (ANIi) and over 176 s in order to obtain the mean ANI (ANIm).
Thus, the ANI monitor provides a number from 0 to 100, which represents the normalized HF oscillations magnitude [14][15][16] . This index is, therefore, strongly correlated to HFnu.
The ANI monitor only provides Energy and ANI parameters and doesn't allow the computation of VLF, LF, HL, and HFnu. In this study, HRV analysis will therefore be limited to ANI and Energy.
Measurements and data handling. HRV was recorded using the ANI monitor. For this, the specific ANI monitor electrodes for ECG were placed on the patient's chest or back, depending on whether the patients were in a supine or prone position. ANIm and mean Energy values were collected for 300 s (5 min), from a single measurement in the morning before daily washing. Any changes in drugs potentially affecting HRV and invasive procedures were avoided too.
To assess the degree of sedation, the RASS scale 12,17,18 was used, and electroencephalogram monitoring with bispectral index (BIS), if it was available at the institution 17,18 . To estimate the severity, the SOFA score was used -the gold-standard score for organ failure in critically ill patients 19 . A blood test for CRP, procalcitonin, and IL-6, was performed on the morning of the same day of the HRV measure and the values were obtained from the central hospital laboratory of each institution.
Analgosedation depended on the criteria of the clinician in charge of the patient. It was maintained using continuous infusions of different drugs, according to routine clinical practice and the usual department protocol of each institution. These protocols include midazolam (0.03-0.2 mg kg h 1 ), propofol (0.5-4 mg kg h 1 ), morphine (0.5-5 mg h −1 ), fentanyl (1-10 mcg kg h −1 ), remifentanil (0.05-0.2 mcg kg min −1 ), or dexmedetomidine (0.4-1.4 mcg kg h −1 ). In cases where neuromuscular blocking was needed, it was used a continuous perfusion of cisatracurium (0.06-0.3 mg kg h −1 ) or rocuronium (0.3-0.6 mg kg h 1 ). Likewise, mechanical ventilation was www.nature.com/scientificreports/ personalized for each patient according to the severity of illness and gasometric analytical parameters, as per department protocols. Subsequently, within 30 days after all these data collections, the patient's electronic medical record was checked and the date of hospital admission, date of admission to ICU, date of discharge to a hospital facility, survival days, or date of death was recorded. According to the 30 days mortality after data collection, the patients were categorized into the non-survivor group and the survivor group.
Sample size. Patient inclusion was performed by sequential review of cases in a predetermined 12-month recruitment period from September 2020 to September 2021. In terms of post-hoc power calculations, a sample size of 112 subjects yields more than 95% power to declare a significant difference in the distribution of ANIm and Energy (SDNN) scores assuming the distributed variables estimated in the study.

Statistical analysis.
To analyze the data, continuous variables normality was assessed using a Shapiro-Wilk test. Normally distributed variables are expressed as mean and standard deviation. Non-normally distributed variables are expressed as median and 1st; 3rd quartile. Categorical variables are expressed as frequency. The difference in continuous variables between survival and deceased groups was assessed using a Student T-Test or a non-parametric Mann-Whitney test according to normality. The difference in categorical variables between survival and deceased groups was assessed using a Chi-square (χ 2 ) or exact Fisher test. Difference between variables was assessed using a Box Plot and receiver operating characteristic curves (ROC) were assessed for variables showing significant differences. The correlations between variables were assessed with a Pearson test. p-value less than 0.05 was considered statistically significant. To evaluate the ability to discriminate survival from deceased patients, a ROC analysis was performed for any variables showing a p < 0.05. The limit value, as well as sensitivity, specificity, positive predictive, and negative values, were determined for any variable showing an AUC > 0.7. Finally, a multivariate discriminant analysis including ANIm, Energy, age, SOFA, and RASS was performed. Apple Numbers version 10.3.9 was used to collect data and all statistical analyses were performed using the SPSS statistics 20.0 (IBM, Armonk, NY) software.
Ethics approval and consent to participate. The study was approved by the Ethical and Research Committee of Mostoles General University Hospital (Madrid, Spain), registered No. 2020/035. This study was performed in line with the principles of the Declaration of Helsinki and written informed consent was obtained from all the institutions and all the subjects by the legal designees.

Results
During the study period, 112 patients were collected in five hospitals from Mexico, Colombia, France, and Spain. Patients were divided into two groups, the survival group (n = 55) and the deceased group (n = 57), depending on the 30 days mortality. Of these 112 patients, 55 were female (49.1%) and 57 were male (50.9%); the mean age was 61 years (range 18-85 years).
The survival and deceased groups showed demographic differences in age, the use of neuromuscular blockers, noradrenaline, anticoagulants, and antibiotics; and type of mechanical ventilation, volume-controlled ventilation, and pressure-controlled ventilation as shown in Table 1.

Heart rate variability. Both ANIm and Energy (SDNN) figures were statistically different between groups
and correlated with the disease severity. The ANIm figures were higher in the deceased group, with p = 0.013; survival group 66 (15) and deceased group 74 (17) (Fig. 2). Likewise, Energy (SDNN) was lower in the deceased group, with p = 0.001; survival group 0.587 (0.432) s and deceased group 0.322 (0.351) s (Fig. 2).
When we analyzed the SOFA score between groups, there were significant differences between them, as expected, with p = 0.017; survival group 7.80 (2.87) and deceased group 9.23 (3.36). In our sample of patients, neither ANIm (r = − 0.099, p = 0.300) nor Energy (SDNN) (r = − 0.072, p = 0.450) were correlated with the SOFA score.
For the relationship between the level of analgosedation, measured by the RASS score, we found that higher ANIm values were correlated with deeper levels of sedation (r = 0.246, p = 0.009). However, we found no relationship between Energy and RASS (r = − 0.099, p = 0.301).
Predictive analysis for mortality in COVID-19 critically ill patients. If we analyze the relationship between HRV variables and the prognostic values for survival, we found that higher Energy levels, i.e., higher SDNN, were correlated with higher survival days (r = 0.388, p = 0.009). We found no correlation between Energy and days to extubation or days to discharge in the survival group. Otherwise, ANIm was not correlated to any prognostic value (Table 2).
To attempt to predict the risk of mortality and for the calculation of diagnostic accuracy, the corresponding ROC curves were analyzed for Energy (SDNN), and SOFA scale. For Energy, we found that a limit value of 0.31 s www.nature.com/scientificreports/ predicted mortalities with a sensitivity of 71.9%, a specificity of 74.5%, a positive predictive value of 74.5%, and a negative predictive value of 71.9% (Fig. 3).
If we compare the ROC curve of Energy (Fig. 3 left) with the AUC = 0.755 and the ROC curve of the SOFA score (Fig. 3 right) with the AUC = 0.636, we discovered that the predictive accuracy of Energy. i.e., SDNN, in predicting mortality is higher than the SOFA score in this kind of patient.  www.nature.com/scientificreports/ Our multivariate analysis was performed using the ANIm, SDNN (Energy), age, SOFA score, and the RASS scale. In this case, the sensitivity increased to 73.7%, the specificity increased to 75.5%, the positive predictive value increased to 75%, the negative predictive value increased to 73.2% and the AUC was 0.817. For this multivariate analysis, 74.1% of the records were correctly classified (Fig. 4).

Discussion
To our knowledge, this study is the first prospective, multicenter, international study that studies the monitoring of HRV as a predictor of severity, survival, and mortality in critically ill patients on mechanical ventilation with coronavirus. The results of this research are consistent with and clarify the data found in our previous pilot study 9 . Autonomic dysregulation in the patient with SARS-COV-2. Agreeing with our results 9 , Mol et al.
and Pan et al. 7,8 concluded that a decrease in HRV, measured by SDNN, can be used as a biomarker to predict severity at different stages of the COVID-19.
The cause of the autonomic dysfunction and the decrease in HRV seems to be multifactorial 8,20 . As we can see in our results and other studies, SDNN (Energy) is a marker of health status 20 that also may vary with age 7 , with the neurotrophic effect of SARS-CoV-2 itself 1 , with cardiological autonomic involvement 21 , with bacterial superinfection 22 , and with the evolution of the COVID-19 20,[23][24][25] .
These studies conclude that in the first acute phase of the disease, the most severe patients have a predominance of the sympathetic nervous system compared with the parasympathetic, with a higher ratio of low to high-frequency power (LF/HF) 7,8 . This first acute phase would correspond to an overactive immune response and excessive hyper-inflammation that follows the incubation of SARS-CoV-2 7 , which is mediated by the sympathetic nervous system 26,27 . During this pro-inflammatory response, there is a large discharge of inflammatory cytokines, with activation of the hypothalamic-pituitary-adrenal axis and a large adrenergic and cortisol release 2,3,28 .
After this first proinflammatory stage, and during the post-acute compensatory anti-inflammatory response (CARS) [26][27][28] , there is a substantial adrenergic and corticosteroid hormone deficiency, with sympathetic nervous system depletion and immune anergy, which results in the immune system being ineffective in controlling the virus, leading to fatal multi-organ failure [1][2][3]25 .
This can be confirmed in our results, where in the last phase of the disease there is a depletion of the ANS with lower Energy (SDNN). On the other hand, the higher ANIm observed in the deceased group compared with the survivors should be explained by a depletion of the sympathetic nervous system leading to a predominance of the parasympathetic system. This pattern can also be seen in patients with poor prognosis and higher mortality with severe sepsis 29 , postoperative patients with acute respiratory distress syndrome (ARDS) 30 , patients after myocardial infarction 31 , chronic heart failure 32 , left ventricular dysfunction 33 , polytraumatized patients 34 , or in chronic diseases 35 . Comparative analysis of energy and SOFA scale as a predictor of mortality. The SOFA score is an international scale, widely used, and currently considered the gold standard for predicting multiorgan failure www.nature.com/scientificreports/ in critically ill patients with sepsis 19 . It is a multiparametric scale, which requires laboratory analytical parameters which are often not available at the patient's bedside 18,19 . Our findings suggest that the Energy (SDNN) can predict mortality with more specificity and sensitivity in this type of patient when it is compared with the SOFA scale. In addition, the Energy values were directly correlated with survival days, i.e., the lower the Energy, the lower the survival days.
Therefore, the analysis of HRV, and more specifically the Energy value, seems to be a non-invasive and more effective way of assessing survival than the SOFA scale, which can be useful as a triage tool or as a complement to inform therapeutic decisions.
ANIm, RASS scale, and analgosedation. The RASS scale, although subjective and physician-dependent, is the scale, along with hemodynamic parameters, most widely used in critical care units to assess the degree of analgosedation and agitation in critically ill patients 18,19 . However, pain scales used in critically ill patients, such as the Campbell scale, require a certain degree of consciousness and muscle tone, and they are, therefore, not recommended in patients under deep sedation and neuromuscular blockade 36 .
In our study, the ANIm value correlated directly with the RASS scale, i.e., those patients with greater sedation according to the RASS scale had higher ANIm values. The use of HRV is already widely used in the operating room to monitor the imbalance between nociception and anti-nociception, and numerous articles, such as the study by Boselli et al. in patients with COVID-19 37 , conclude that the use of spectral analysis of HRV with ANIm using the ANI monitor can be a reliable biomarker to monitor nociception in the critically ill patient with COVID-19.
ANS control and analgosedation. Moreover, as suggested by Upton et al. 38 in their clinical trial and by most international guidelines 6,36,39 , excessive analgosedation in both the critically ill and the surgical patient can lead to significant depletion of the sympathetic nervous system, and ultimately to greater complications and worse disease outcome.
As previously mentioned, the modulation and control of the ANS are multifactorial. There are different mechanisms to modulate the autonomic nervous system of our patients such as vagus nerve stimulation 40 , transcranial stimulation 41 , control of inspiration and expiration 42 , or even by the control of the nociceptive autonomic medullary circuit 13,[26][27][28]40 .
It is possible to modulate by analgosedation drugs the nociceptive autonomic medullary circuit, responsible for activating the nucleus of the solitary tract. This nucleus of the solitary tract, in the case of any nociceptive, inflammatory, or infectious stimulus, is the subcortical center that controls the sympathetic response, and therefore, the pro-inflammatory response. It also controls the activation of the vagus nerve and the consequent activation of the anti-inflammatory cholinergic chain 13,26,28 .
In our study, we have seen that a significant depletion of the sympathetic nervous system and an excess of parasympathetic activity predicts a worse progression of the disease and higher mortality. Both overdosing and underdosing of analgosedation can, therefore, lead to worse patient outcomes.
We must individualize analgosedation for each patient taking into account several objective factors. Firstly, the state of the CNS, using EEG spectral analysis, minimizes the possible neurological damage derived from an excess of cortical suppression 36,39,42 . Secondly, we must monitor the autonomic nervous system using HRV to determine the state of the sympathetic and parasympathetic nervous system, i.e., ANIm, and maintain a balance and homeostasis between both. And thirdly, to monitor the severity and fragility of the patient, measured through the Energy (SDNN), to titrate and adjust the dosage more precisely in those patients who were more fragile and with greater depletion of the ANS 7-9,38 .
As suggested by different studies 43,44 and international protocols 36,39 , we should promote multimodal analgosedation in critically ill patients, minimizing the use of lipophilic opioid drugs, like fentanyl, and using adjuvant drugs, such as ketamine or dexmedetomidine, to control this autonomic response.
It is well known that ketamine has a certain sympathomimetic effect and could, therefore, be useful as an adjuvant in patients with hemodynamic instability and certain autonomic depletion 44 . On the other hand, dexmedetomidine, an alpha-2-agonist drug that acts on the locus ceruleus inhibiting the "fight or flight" adrenergic response, is an important sympatholytic, which should be reserved in critical patients in an early stage with a significant predominance of the sympathetic nervous system, or a later stage in the weaning phase 39,43 , limiting it in more fragile patients with worse prognosis, with less Energy (SDNN).
Limitations. Despite its strengths, there may be some possible limitations in this study that must be taken into account. Primarily, this is an observational study, with no randomization possibility. Furthermore, our study is a multicenter study and therefore the different analgosedation strategies depend on the clinical criteria and the protocols of each hospital center. Mechanical ventilation was personalized for each patient according to the severity of illness which can constitute a bias in ANI interpretation. However, Jeanne et al. 16 demonstrated that the normalization process strongly limits the effect of the respiratory volume and that the graphical assessment (i.e. Area under the envelopes computation) strongly limited the effect of the respiratory rate.
In future studies, we plan to analyze in a randomized manner whether or not monitoring HRV improves the titration of analgosedation and improves the evolution of critically ill patients. Furthermore, a retrospective analysis including other HRV common markers (HF, LF, HFnu) would allow us to better explain the physiopathological link between HRV and COVID-19 patients' severity. The inclusion and calculation of other HRV metrics in a future analysis should lead to better sensitivity/specificity in predicting mortality and further validate the present results.

Conclusion
According to the results of our study, we conclude that HRV monitoring in critically ill patients with coronavirus allows predicting, with high sensitivity and specificity, survival days and mortality through Energy. We demonstrated that the use of the Energy (SDNN) value by itself, and together with other parameters by multivariate analysis with age, the RASS scale, and the ANIm value, allows predicting with higher specificity and higher sensitivity the mortality of critically ill patients with COVID-19 in comparison with the SOFA scale. We further concluded that frequency domain parameters such as LF (sympathetic) and HF (parasympathetic) also vary according to the stage and severity of the disease. According to our sample, those patients with greater severity (i.e., those patients who did not survive) showed a predominance of relative parasympathetic activity (i.e., ANIm value).
In summary, on the basis of our results and those of other studies, the recommendation to control the immune and inflammatory system by modulating the ANS of our critically ill patients needs to be considered. To this end, we suggest reviewing our analgosedation protocols in critically ill patients, controlling the balance between sympathetic and parasympathetic by analysis of HRV. We also encourage individualizing the dosage of analgosedation for each patient, titrating and using the minimum effective dose, avoiding under-and overdosing.

Data availability
The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.